A novel Bayesian statistical methodology for spatial survival data is proposed. The methodology broadens the definition of the survival, density and hazard functions by explicitly modeling
the spatial dependency using direct derivations of these functions and their marginals and conditionals. Spatially dependent likelihood functions are also derived and simulations to compare my
method with the random effects model are also given. Finally, the application of these derivations
with geographically augmented survival distributions in the context of the Louisiana Surveillance,
Epidemiology and End Results (SEER) registry prostate cancer data is discussed